Recently, studies and developments of Collision Warning System (CWS) have attracted widely a particular attention among various wave applications at 60 GHz frequency band. Major performance of the CWS is given by the maximum detection range, the minimum detectable width and accuracy of detection distance. Since the maximum detection range and the minimum detectable width depend on a size of a target, it is important to know Radar Cross Section (RCS) of the target. RCS data should be measured so-called far field condition (greater than a few hundreds meters), while CWS is usually used in the range of 20-100 m. Therefore, a measurement of the reflection characteristics in shorter distance should be necessarily known. In this study, we present reflection characteristics of a medium size truck and a passenger car measured in open field at 60 GHz frequency. It is concludingly described that the reflection characteristics of the passenger car and the medium size truck are 13-18 dBm2 and 15-32 dBm2, respectively, for a case of horizontal polarization.

In this paper we propose a new far-field RCS prediction method using cylindrical or planar near-field RCS data. First we derive the relation between RCS and the scattering coefficient using physical optics technique. The far-field RCS prediction algorithm is obtained by approximating the relation using the condition of Fresnel region and the paraxial constraint of scanning angle in the case of cylindrical or planar scanning. Finally we predict the far-field RCS using measured or calculated near-field RCS data of the conducting rectangular prism or plate. The validity of the proposed algorithm is demonstrated.

We propose a method for accomplishing accurate RCS (Radar Cross Section ) images of a car in a compact range. It is an improved method based on an ISAR (Inverse Synthetic Aperture Radar) technique. To obtain accurate RCS values, an idea of an image correction function for the Fourier transform used in the ISAR processing is introduced. The role of the image correction function is to compensate the difference of the propagation loss as to the different scattering points on a target. As a result, `sensitivity' of imaging in the compact range is kept uniform. Hamming window is suitable for the Fourier transform to accomplish RCS images because of its low sidelobe level and the sharpness of a mainlobe. When hamming window is adopted, the spatial resolution is approximately twice the size of granularity which is determined by the ISAR parameters. To verify the improvement of the RCS images obtained by means of our method, several numerical target models are employed. The results of the investigation show that uniformity of `sensitivity' for obtained RCS images is achieved in the compact range and accurate images with the resolution of twice the size of granularity are accomplished without blurs or distortions in the unambiguous area. RCS images for rear aspects of a passenger car are investigated with the spatial resolution of 50 mm in the 60 GHz band. The RCS image varies with the aspect angle of the car and the specular reflection occurs for the millimeter wave. When the curvature on the car edge is small, a blurred RCS image is observed. The reason is that a scattering center of the specular reflection moves so widely that it can't be regarded as a fixed point. This causes elongation of the RCS image. A peak value in the dominant area for each aspect angle is less the 0 dBsm and no remarkable areas where the RCS value exceeds-20 dBsm is found any more on the car except such the dominant area.

RCS fluctuation of targets such as ships can be reduced by the high-resolution radar. The high-resolution radar resolves the total radar echo into several parts which do not interfere each other. The reduction of interference gives stable target RCS. A simple model of the RCS fluctuation reduction is presented. Typical data for ships taken by an experimental radar which has range resolution about 0.75 m, are also shown. The analysis results show that the RCS fluctuation reduction agree with the simple model well.